SOTAVerified

Personalized Image Generation

Utilizes single or multiple images that contain the same subject or style, along with text prompt, to generate images that contain that subject as well as match the textual description. Includes finetuning-based methods (e.g. DreamBooth, Textual Inversion) as well as encoder-based methods (e.g. E4T, ELITE, and IP-Adapter, etc.).

Papers

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Showing 21–30 of 58 papers

TitleStatusHype
Personalized Image Generation with Large Multimodal ModelsCode1
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved PersonalizationCode0
Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image GenerationCode2
Imagine yourself: Tuning-Free Personalized Image Generation—0
StoryMaker: Towards Holistic Consistent Characters in Text-to-image GenerationCode4
EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidanceCode2
TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text EncoderCode2
ViPer: Visual Personalization of Generative Models via Individual Preference Learning—0
Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation—0
DreamBench++: A Human-Aligned Benchmark for Personalized Image GenerationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DreamBooth LoRA SDXL v1.0Overall (CP * PF)0.52—Unverified
2IP-Adapter ViT-G SDXL v1.0Overall (CP * PF)0.38—Unverified
3Emu2 SDXL v1.0Overall (CP * PF)0.36—Unverified
4DreamBooth SD v1.5Overall (CP * PF)0.36—Unverified
5IP-Adapter-Plus ViT-H SDXL v1.0Overall (CP * PF)0.34—Unverified
6BLIP-Diffusion SD v1.5Overall (CP * PF)0.27—Unverified
7Textual Inversion SD v1.5Overall (CP * PF)0.24—Unverified